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chore: Add manual openLLMetry example (#1143)
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‎package.json‎

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"packages/sdk/server-ai/examples/openai",
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"packages/sdk/server-ai/examples/tracked-chat",
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"packages/sdk/server-ai/examples/chat-observability",
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"packages/sdk/server-ai/examples/openai-observability",
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"packages/sdk/server-ai/examples/vercel-ai",
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"packages/telemetry/browser-telemetry",
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"packages/sdk/combined-browser",

‎packages/sdk/server-ai/examples/openai-observability/.env.example‎

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# LaunchDarkly SDK Key (required)
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LAUNCHDARKLY_SDK_KEY=your-launchdarkly-sdk-key-here
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# AI Config key (optional, defaults to 'sample-ai-config')
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LAUNCHDARKLY_AI_CONFIG_KEY=sample-ai-config
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# Observability service identification (optional)
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SERVICE_NAME=hello-js-openai-observability
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SERVICE_VERSION=1.0.0
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# OpenAI API Key (required)
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OPENAI_API_KEY=your-openai-api-key-here

‎packages/sdk/server-ai/examples/openai-observability/README.md‎

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# Provider-Specific Observability Example (OpenAI)
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This example shows how to use the LaunchDarkly observability plugin when calling an AI provider directly — without the higher-level `createChat` abstraction. It uses OpenAI as the provider, but the same pattern applies to any provider (Bedrock, Anthropic, Vercel AI SDK, etc.).
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## How it works
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1. **Initialize the LaunchDarkly client** with the `Observability` plugin — this enables automatic capture of SDK operations, flag evaluations, errors, logs, and distributed traces.
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2. **Get the AI Config** via `completionConfig()` — this returns the model, messages, and parameters configured in LaunchDarkly, along with a `tracker` for reporting metrics.
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3. **Call your provider directly** and wrap it with the tracker — the tracker records latency, token usage, and success/error status.
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The tracker provides several methods depending on your provider. This example uses `trackMetricsOf` with the LaunchDarkly OpenAI provider's `getAIMetricsFromResponse` extractor:
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| Method | Provider |
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|--------|----------|
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| `tracker.trackMetricsOf(OpenAIProvider.getAIMetricsFromResponse, fn)` | OpenAI (recommended) |
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| `tracker.trackBedrockConverseMetrics(response)` | AWS Bedrock |
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| `tracker.trackVercelAISDKGenerateTextMetrics(fn)` | Vercel AI SDK |
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| `tracker.trackMetricsOf(extractor, fn)` | Any provider (custom extractor) |
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## Prerequisites
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1. A LaunchDarkly account and SDK key
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2. Node.js 16 or later
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3. Node server SDK v9.10 or later (required for the observability plugin)
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4. An OpenAI API key
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## Setup
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1. Install dependencies:
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```bash
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yarn install
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```
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2. Set up environment variables:
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```bash
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cp .env.example .env
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```
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Edit `.env` and add your keys.
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3. Create an AI Config in LaunchDarkly (e.g. key `sample-ai-config`) with a completion-enabled variation and the model you want to use.
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## Running the Example
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```bash
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yarn start
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```
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This will:
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- Initialize the LaunchDarkly client with the observability plugin
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- Retrieve the AI Config (model, messages, parameters) from LaunchDarkly
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- Call OpenAI directly using your own client
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- Automatically track latency, token usage, and success/error via the tracker
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View your data in the LaunchDarkly dashboard under **Observability**.
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## Adapting for other providers
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To use a different provider, replace the OpenAI-specific parts:
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1. Swap the OpenAI client for your provider's client
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2. Use the appropriate tracker method (see table above), or use `trackMetricsOf` with a custom metrics extractor
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3. Map `aiConfig.messages` and `aiConfig.model` to your provider's API format
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See the [bedrock](../bedrock/) example for an AWS Bedrock adaptation.

‎packages/sdk/server-ai/examples/openai-observability/package.json‎

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{
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"name": "openai-observability-example",
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"version": "1.0.0",
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"description": "LaunchDarkly AI SDK example: provider-specific observability with OpenAI",
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"scripts": {
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"build": "tsc",
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"start": "yarn build && node ./dist/index.js"
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},
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"dependencies": {
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"@launchdarkly/node-server-sdk": "workspace:^",
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"@launchdarkly/observability-node": "^1.0.0",
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"@launchdarkly/server-sdk-ai": "workspace:^",
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"@launchdarkly/server-sdk-ai-openai": "workspace:^",
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"@opentelemetry/instrumentation": "^0.57.0",
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"@traceloop/instrumentation-openai": "^0.22.0",
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"dotenv": "^16.0.0",
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"openai": "^5.12.2"
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},
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"devDependencies": {
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"@types/node": "^20.0.0",
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"typescript": "^5.0.0"
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}
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}

‎packages/sdk/server-ai/examples/openai-observability/src/index.ts‎

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/* eslint-disable no-console */
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import { registerInstrumentations } from '@opentelemetry/instrumentation';
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import { OpenAIInstrumentation } from '@traceloop/instrumentation-openai';
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import 'dotenv/config';
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import { init, type LDContext } from '@launchdarkly/node-server-sdk';
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import { Observability } from '@launchdarkly/observability-node';
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import { initAi } from '@launchdarkly/server-sdk-ai';
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const sdkKey = process.env.LAUNCHDARKLY_SDK_KEY;
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const aiConfigKey = process.env.LAUNCHDARKLY_AI_CONFIG_KEY || 'sample-ai-config';
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if (!sdkKey) {
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console.error('*** Please set the LAUNCHDARKLY_SDK_KEY env first');
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process.exit(1);
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}
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// ── 1. Initialize the LaunchDarkly client with the Observability plugin ──
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// The plugin automatically captures SDK operations, flag evaluations,
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// error monitoring, logging, and distributed tracing.
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const ldClient = init(sdkKey, {
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plugins: [
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new Observability({
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serviceName: process.env.SERVICE_NAME || 'hello-js-openai-observability',
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serviceVersion: process.env.SERVICE_VERSION || '1.0.0',
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}),
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],
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});
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registerInstrumentations({
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instrumentations: [new OpenAIInstrumentation()],
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});
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const context: LDContext = {
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kind: 'user',
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key: 'example-user-key',
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name: 'Sandy',
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};
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async function main() {
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try {
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await ldClient.waitForInitialization({ timeout: 10 });
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console.log('*** SDK successfully initialized');
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} catch (error) {
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console.error(`*** SDK failed to initialize: ${error}`);
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process.exit(1);
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}
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const aiClient = initAi(ldClient);
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// ── 2. Import provider and OpenAI after instrumentation so OpenLLMetry can patch the client ──
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const { OpenAIProvider } = await import('@launchdarkly/server-sdk-ai-openai');
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const { OpenAI } = await import('openai');
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const openai = new OpenAI({
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apiKey: process.env.OPENAI_API_KEY,
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});
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// ── 3. Get the AI Config (model, messages, parameters) from LaunchDarkly ──
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// `completionConfig` returns the resolved configuration plus a `tracker`
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// that you use to report metrics back to LaunchDarkly.
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const aiConfig = await aiClient.completionConfig(
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aiConfigKey,
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context,
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{
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model: { name: 'gpt-4' },
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enabled: false,
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},
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{ example_type: 'provider_observability_demo' },
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);
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if (!aiConfig.enabled || !aiConfig.tracker) {
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console.log('*** AI configuration is not enabled');
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ldClient.close();
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process.exit(0);
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}
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try {
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// ── 4. Call OpenAI and track metrics with the provider's extractor ──
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const completion = await aiConfig.tracker.trackMetricsOf(
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OpenAIProvider.getAIMetricsFromResponse,
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() =>
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openai.chat.completions.create({
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messages: aiConfig.messages || [],
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model: aiConfig.model?.name || 'gpt-4',
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temperature: (aiConfig.model?.parameters?.temperature as number) ?? 0.5,
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max_tokens: (aiConfig.model?.parameters?.maxTokens as number) ?? 4096,
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}),
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);
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console.log('AI Response:', completion.choices[0]?.message.content);
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console.log('\nSuccess.');
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} catch (err) {
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console.error('Error:', err);
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} finally {
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ldClient.close();
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}
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}
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main();

‎packages/sdk/server-ai/examples/openai-observability/tsconfig.json‎

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{
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"compilerOptions": {
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"target": "ES2022",
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"module": "CommonJS",
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"moduleResolution": "node",
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"esModuleInterop": true,
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"allowSyntheticDefaultImports": true,
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"strict": true,
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"skipLibCheck": true,
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"forceConsistentCasingInFileNames": true,
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"outDir": "./dist",
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"rootDir": "./src",
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"declaration": true,
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"sourceMap": true
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},
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"include": ["src/**/*"],
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"exclude": ["node_modules", "dist"]
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}

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